Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies

Nature Genetics volume 55, pages 154–164 (2023)Cite this article

Subjects

Abstract

Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medicine (TOPMed) Program, we show that MetaSTAAR performs rare variant meta-analysis at scale and produces results comparable to using pooled data. Additionally, we identified several conditionally significant rare variant associations with lipid traits. We further demonstrate that MetaSTAAR is scalable to biobank-scale cohorts through meta-analysis of TOPMed WGS data and UK Biobank WES data of ~200,000 samples.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: MetaSTAAR workflow.

Data availability

This paper used the TOPMed Freeze 5 WGS data and lipids phenotype data. Genotype and phenotype data are both available in database of Genotypes and Phenotypes. The TOPMed WGS data were from the following fourteen study cohorts (accession numbers provided in parentheses): Framingham Heart Study (phs000974.v1.p1); Old Order Amish (phs000956.v1.p1); Jackson Heart Study (phs000964.v1.p1); and Multi-Ethnic Study of Atherosclerosis (phs001416.v1.p1); Atherosclerosis Risk in Communities Study (phs001211); Cleveland Family Study (phs000954); Cardiovascular Health Study (phs001368); Diabetes Heart Study (phs001412); Genetic Study of Atherosclerosis Risk (phs001218); Genetic Epidemiology Network of Arteriopathy (phs001345); Genetics of Lipid Lowering Drugs and Diet Network (phs001359); San Antonio Family Heart Study (phs001215); Genome-wide Association Study of Adiposity in Samoans (phs000972) and Women’s Health Initiative (phs001237). The sample sizes, ancestry and phenotype summary statistics of these cohorts are given in Supplementary Table 1. The UK Biobank analyses were conducted using the UK Biobank resource under application 52008.

The functional annotation data are publicly available and were downloaded from the following links: GRCh38 CADD v1.4 (https://cadd.gs.washington.edu/download); ANNOVAR dbNSFP v3.3a (https://annovar.openbioinformatics.org/en/latest/user-guide/download); LINSIGHT (https://github.com/CshlSiepelLab/LINSIGHT); FATHMM-XF (http://fathmm.biocompute.org.uk/fathmm-xf); FANTOM5 CAGE (https://fantom.gsc.riken.jp/5/data); GeneCards (https://www.genecards.org; v4.7 for hg38); and Umap/Bismap (https://bismap.hoffmanlab.org; ‘before March 2020’ version). In addition, recombination rate and nucleotide diversity were obtained from Gazal et al. ref. 51). The whole genome individual functional annotation data was assembled from a variety of sources and the computed annotation PCs are available at the Functional Annotation of Variant-Online Resource (FAVOR) site (https://favor.genohub.org) and the FAVOR database (https://doi.org/10.7910/DVN/1VGTJI)52. The tissue-specific functional annotations were downloaded from ENCODE (https://www.encodeproject.org/report/?type=Experiment).

Code availability

MetaSTAAR is implemented as an open-source R package available at https://github.com/xihaoli/MetaSTAAR and https://content.sph.harvard.edu/xlin/software.html. Data analysis was performed in R (3.5.1). STAAR v0.9.6 and MetaSTAAR v0.9.6 were used in simulation and real data analysis and implemented as open-source R packages available at https://github.com/xihaoli/STAAR (ref. 53) and https://github.com/xihaoli/MetaSTAAR (ref. 54). The scripts used to generate the results have been archived on Zenodo using https://doi.org/10.5281/zenodo.6668274 (ref. 55). RareMetal v4.15.1 (https://github.com/statgen/raremetal) and GMMAT v1.3.2 (https://cran.r-project.org/web/packages/GMMAT/index.html) were used for comparison. The assembled functional annotation data were downloaded from FAVOR using Wget (https://www.gnu.org/software/wget/wget.html).

References

  1. Taliun, D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590, 290–299 (2021).

    Article  CAS  Google Scholar 

  2. Van Hout, C. V. et al. Exome sequencing and characterization of 49,960 individuals in the UK Biobank. Nature 586, 749–756 (2020).

    Article  Google Scholar 

  3. Szustakowski, J. D. et al. Advancing human genetics research and drug discovery through exome sequencing of the UK Biobank. Nat. Genet. 53, 942–948 (2021).

    Article  CAS  Google Scholar 

  4. Hindy, G. et al. Rare coding variants in 35 genes associate with circulating lipid levels—a multi-ancestry analysis of 170,000 exomes. Am. J. Hum. Genet. 109, 81–96 (2022).

    Article  CAS  Google Scholar 

  5. Flannick, J. et al. Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls. Nature 570, 71–76 (2019).

    Article  CAS  Google Scholar 

  6. Jurgens, S. J. et al. Analysis of rare genetic variation underlying cardiometabolic diseases and traits among 200,000 individuals in the UK Biobank. Nat. Genet. 54, 240–250 (2022).

    Article  CAS  Google Scholar 

  7. Wainschtein, P. et al. Assessing the contribution of rare variants to complex trait heritability from whole-genome sequence data. Nat. Genet. 54, 263–273 (2022).

    Article  CAS  Google Scholar 

  8. Lee, S., Abecasis, Gonçalo, R., Boehnke, M. & Lin, X. Rare-variant association analysis: study designs and statistical tests. Am. J. Hum. Genet. 95, 5–23 (2014).

    Article  CAS  Google Scholar 

  9. Li, B. & Leal, S. M. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am. J. Hum. Genet. 83, 311–321 (2008).

    Article  CAS  Google Scholar 

  10. Madsen, B. E. & Browning, S. R. A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet. 5, e1000384 (2009).

    Article  Google Scholar 

  11. Morris, A. P. & Zeggini, E. An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet. Epidemiol. 34, 188–193 (2010).

    Article  Google Scholar 

  12. Wu, M. C. et al. Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet. 89, 82–93 (2011).

    Article  CAS  Google Scholar 

  13. Liu, Y. et al. ACAT: a fast and powerful P value combination method for rare-variant analysis in sequencing studies. Am. J. Hum. Genet. 104, 410–421 (2019).

    Article  CAS  Google Scholar 

  14. McCarthy, M. I. et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat. Rev. Genet. 9, 356–369 (2008).

    Article  CAS  Google Scholar 

  15. Evangelou, E. & Ioannidis, J. P. A. Meta-analysis methods for genome-wide association studies and beyond. Nat. Rev. Genet. 14, 379–389 (2013).

    Article  CAS  Google Scholar 

  16. Buniello, et al. The NHGRI-EBI GWAS catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–D1012 (2019).

    Article  CAS  Google Scholar 

  17. Lin, D. Y. & Zeng, D. Meta-analysis of genome-wide association studies: no efficiency gain in using individual participant data. Genet. Epidemiol. 34, 60–66 (2010).

    CAS  Google Scholar 

  18. Lin, D. Y. & Zeng, D. On the relative efficiency of using summary statistics versus individual-level data in meta-analysis. Biometrika 97, 321–332 (2010).

    Article  CAS  Google Scholar 

  19. Liu, D. J. et al. Meta-analysis of gene-level tests for rare variant association. Nat. Genet. 46, 200–204 (2014).

    Article  CAS  Google Scholar 

  20. Feng, S., Liu, D., Zhan, X., Wing, M. K. & Abecasis, G. R. RAREMETAL: fast and powerful meta-analysis for rare variants. Bioinformatics 30, 2828–2829 (2014).

    Article  CAS  Google Scholar 

  21. Lee, S., Teslovich, Tanya, M., Boehnke, M. & Lin, X. General framework for meta-analysis of rare variants in sequencing association studies. Am. J. Hum. Genet. 93, 42–53 (2013).

    Article  CAS  Google Scholar 

  22. Hu, Y.-J. et al. Meta-analysis of gene-level associations for rare variants based on single-variant statistics. Am. J. Hum. Genet. 93, 236–248 (2013).

    Article  CAS  Google Scholar 

  23. Yang, J., Chen, S. & Abecasis, G., IAMDGC. Improved score statistics for meta-analysis in single-variant and gene-level association studies. Genet. Epidemiol. 42, 333–343 (2018).

    Article  Google Scholar 

  24. Chen, H. et al. Efficient variant set mixed model association tests for continuous and binary traits in large-scale whole-genome sequencing studies. Am. J. Hum. Genet. 104, 260–274 (2019).

    Article  CAS  Google Scholar 

  25. Chen, M.-H., Pitsillides, A. & Yang, Q. An evaluation of approaches for rare variant association analyses of binary traits in related samples. Sci. Rep. 11, 3145 (2021).

    Article  CAS  Google Scholar 

  26. Li, X. et al. Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale. Nat. Genet. 52, 969–983 (2020).

    Article  CAS  Google Scholar 

  27. Gogarten, S. M. et al. Genetic association testing using the GENESIS R/Bioconductor package. Bioinformatics 35, 5346–5348 (2019).

    Article  CAS  Google Scholar 

  28. Chen, H. et al. Control for population structure and relatedness for binary traits in genetic association studies via logistic mixed models. Am. J. Hum. Genet. 98, 653–666 (2016).

    Article  CAS  Google Scholar 

  29. Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    Article  CAS  Google Scholar 

  30. Natarajan, P. et al. Deep-coverage whole genome sequences and blood lipids among 16,324 individuals. Nat. Commun. 9, 3391 (2018).

    Article  Google Scholar 

  31. Stilp, A. M. et al. A system for phenotype harmonization in the national heart, lung, and blood institute Trans-omics for Precision Medicine (TOPMed) program. Am. J. Epidemiol. 190, 1977–1992 (2021).

    Article  Google Scholar 

  32. Forrest, A. R. et al. A promoter-level mammalian expression atlas. Nature 507, 462 (2014).

    Article  CAS  Google Scholar 

  33. Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).

    Article  CAS  Google Scholar 

  34. Fishilevich, S. et al. GeneHancer: genome-wide integration of enhancers and target genes in GeneCards. Database 2017, bax028 (2017).

    Article  Google Scholar 

  35. Li, Z. et al. A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies. Nat. Methods (2022). https://doi.org/10.1038/s41592-022-01640-x

  36. Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).

    Article  CAS  Google Scholar 

  37. Huang, Y.-F., Gulko, B. & Siepel, A. Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data. Nat. Genet. 49, 618–624 (2017).

    Article  CAS  Google Scholar 

  38. Rogers, M. F. et al. FATHMM-XF: accurate prediction of pathogenic point mutations via extended features. Bioinformatics 34, 511–513 (2017).

    Article  Google Scholar 

  39. Dong, C. et al. Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum. Mol. Genet. 24, 2125–2137 (2014).

    Article  Google Scholar 

  40. Zhou, H. et al. FAVOR: functional annotation of variants online resource and annotator for variation across the human genome. Nucleic Acids Res. gkac966, https://doi.org/10.1093/nar/gkac966 (2022).

  41. Schaffner, S. F. et al. Calibrating a coalescent simulation of human genome sequence variation. Genome Res. 15, 1576–1583 (2005).

    Article  CAS  Google Scholar 

  42. Lee, P. H. et al. Principles and methods of in-silico prioritization of non-coding regulatory variants. Hum. Genet. 137, 15–30 (2018).

    Article  CAS  Google Scholar 

  43. Morrison, A. C. et al. Practical approaches for whole-genome sequence analysis of heart-and blood-related traits. Am. J. Hum. Genet. 100, 205–215 (2017).

    Article  CAS  Google Scholar 

  44. Li, Z. et al. Dynamic scan procedure for detecting rare-variant association regions in whole-genome sequencing studies. Am. J. Hum. Genet. 104, 802–814 (2019).

    Article  CAS  Google Scholar 

  45. The All of Us Research Program Investigators The “All of Us” Research Program. N Engl J Med 381, 668–676 (2019).

    Article  Google Scholar 

  46. Klarin, D. et al. Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program. Nat. Genet. 50, 1514–1523 (2018).

    Article  CAS  Google Scholar 

  47. Breslow, N. E. & Clayton, D. G. Approximate inference in generalized linear mixed models. J. Am. Stat. Assoc. 88, 9–25 (1993).

    Google Scholar 

  48. Jiang, L. et al. A resource-efficient tool for mixed model association analysis of large-scale data. Nat. Genet. 51, 1749–1755 (2019).

    Article  CAS  Google Scholar 

  49. Jiang, L., Zheng, Z., Fang, H. & Yang, J. A generalized linear mixed model association tool for biobank-scale data. Nat. Genet. 53, 1616–1621 (2021).

    Article  CAS  Google Scholar 

  50. Quick, C. et al. A versatile toolkit for molecular QTL mapping and meta-analysis at scale. Preprint at bioRxiv https://doi.org/10.1101/2020.12.18.423490 (2020).

  51. Gazal, S. et al. Linkage disequilibrium–dependent architecture of human complex traits shows action of negative selection. Nat. Genet. 49, 1421–1427 (2017).

    Article  CAS  Google Scholar 

  52. Zhou, H., Arapoglou, T., Li, X., Li, Z. & Lin, X. FAVOR Essential Database. V1 Edition (Harvard Dataverse, 2022).

  53. Li, X., Li, Z. & Chen, H. xihaoli/STAAR: STAAR_v0.9.6. Version 0.9.6 https://doi.org/10.5281/zenodo.6960622 (2022)

  54. Li, X. & Li, Z. xihaoli/MetaSTAAR: MetaSTAAR_v0.9.6. Version 0.9.6 https://doi.org/10.5281/zenodo.6960606 (2022)

  55. Li, X., Li, Z. & Lin, X. MetaSTAAR. Version 1 https://doi.org/10.5281/zenodo.6668274 (2022)

Download references

Acknowledgements

This work was supported by grants R35-CA197449, U19-CA203654, R01-HL113338, U01-HG012064 and U01-HG009088 (X. Lin), NHLBI BioData Catalyst Fellowship (Z.L.), R01-HL142711 and R01-HL127564 (P.N. and G.M.P.), 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-000040, UL1-TR-001079, UL1-TR-001420, UL1-TR001881, DK063491, R01-HL071051, R01-HL071205, R01-HL071250, R01-HL071251, R01-HL071258, R01-HL071259 and UL1-RR033176 (J.I.R. and X.G.), R35-HL135824 (C.J.W.), U01-HL72518, HL087698, HL49762, HL59684, HL58625, HL071025, HL112064, NR0224103 and M01-RR000052 (to the Johns Hopkins General Clinical Research Center), NO1-HC-25195, HHSN268201500001I, 75N92019D00031 and R01-HL092577-06S1 (R.S.V. and L.A.C.), the Evans Medical Foundation and the Jay and Louis Coffman Endowment from the Department of Medicine, Boston University School of Medicine (R.S.V.), HHSN268201800001I and U01-HL137162 (K.M.R.), R01-HL093093 and R01-HL133040 (S.T.M.), R35-HL135818, R01-HL113338 and HL436801 (S.R.), KL2TR002490 (L.M.R.), R01-HL92301, R01-HL67348, R01-NS058700, R01-AR48797 and R01-AG058921 (N.D.P. and D.W.B.), R01-DK071891 (N.D.P., B.I.F. and D.W.B.), M01-RR07122 and F32-HL085989 (to the General Clinical Research Center of the Wake Forest University School of Medicine), the American Diabetes Association, P60-AG10484 (to the Claude Pepper Older Americans Independence Center of Wake Forest University Health Sciences), U01-HL137181 (J.R.O.), HHSN268201600018C, HHSN268201600001C, HHSN268201600002C, HHSN268201600003C and HHSN268201600004C (C.K.), R01-HL113323, U01-DK085524, R01-HL045522, R01-MH078143, R01-MH078111 and R01-MH083824 (H.H.H.G., R.D., J.E.C. and J.B.), 18CDA34110116 from American Heart Association (P.S.d.V.), HHSN268201800010I, HHSN268201800011I, HHSN268201800012I, HHSN268201800013I, HHSN268201800014I and HHSN268201800015I (A.C.), R01-HL153805, R03-HL154284 (B.E.C.), HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700005I and HHSN268201700004I (E.B.), U01-HL072524, R01-HL104135-04S1, U01-HL054472, U01-HL054473, U01-HL054495, U01-HL054509 and R01-HL055673-18S1 (D.K.A.). Molecular data for the Trans Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung and Blood Institute (NHLBI). Core support including centralized genomic read mapping and genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (3R01HL-117626-02S1; contract HHSN268201800002I). Core support including phenotype harmonization, data management, sample-identity QC and general program coordination was provided by the TOPMed Data Coordinating Center (R01HL-120393; U01HL-120393; contract HHSN268201800001I). We gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed and UK Biobank. The full study-specific acknowledgements and NHLBI BioData Catalyst acknowledgement are detailed in the Supplementary Note.

Author information

Author notes

  1. These authors jointly supervised this work: Zilin Li, Xihong Lin.

Authors and Affiliations

  1. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Xihao Li, Corbin Quick, Hufeng Zhou, Sheila M. Gaynor, Rounak Dey, Zilin Li & Xihong Lin

  2. School of Statistics, Southwestern University of Finance and Economics, Chengdu, China

    Yaowu Liu

  3. Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA

    Han Chen, Eric Boerwinkle, Paul S. de Vries & Alanna C. Morrison

  4. Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA

    Han Chen

  5. Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA

    Margaret Sunitha Selvaraj & Pradeep Natarajan

  6. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Margaret Sunitha Selvaraj, Brian E. Cade, Pradeep Natarajan & Xihong Lin

  7. Department of Medicine, Harvard Medical School, Boston, MA, USA

    Margaret Sunitha Selvaraj & Pradeep Natarajan

  8. Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Ryan Sun

  9. University of Kentucky, College of Public Health, Lexington, KY, USA

    Donna K. Arnett

  10. Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA

    Lawrence F. Bielak, Patricia A. Peyser, Jennifer A. Smith & Wei Zhao

  11. Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA

    Joshua C. Bis, Jennifer A. Brody, Bruce M. Psaty & Colleen M. Sitlani

  12. Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA

    John Blangero, Joanne E. Curran, Ravindranath Duggirala & Harald H. H. Göring

  13. Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA

    Eric Boerwinkle

  14. Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA

    Donald W. Bowden & Nicholette D. Palmer

  15. Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA

    Brian E. Cade & Susan Redline

  16. Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA

    Brian E. Cade & Susan Redline

  17. Jackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA

    Adolfo Correa

  18. Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA

    L. Adrienne Cupples & Gina M. Peloso

  19. Framingham Heart Study, National Heart, Lung, and Blood Institute and Boston University, Framingham, MA, USA

    L. Adrienne Cupples & Ramachandran S. Vasan

  20. Department of Internal Medicine, Nephrology, Wake Forest University School of Medicine, Winston-Salem, NC, USA

    Barry I. Freedman

  21. The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor–UCLA Medical Center, Torrance, CA, USA

    Xiuqing Guo, Kent D. Taylor & Jerome I. Rotter

  22. Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA

    Jeffrey Haessler, Charles Kooperberg, Alexander P. Reiner & Ulrike Peters

  23. GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Rita R. Kalyani, Brian G. Kral & Lisa R. Yanek

  24. Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

    Leslie A. Lange

  25. Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA

    Ani Manichaikul & Stephen S. Rich

  26. Division of Cardiology, George Washington School of Medicine and Health Sciences, Washington, DC, USA

    Lisa W. Martin

  27. Department of Epidemiology, International Health Institute, Department of Anthropology, Brown University, Providence, RI, USA

    Stephen T. McGarvey

  28. Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA

    Braxton D. Mitchell & Jeffrey R. O’Connell

  29. Geriatrics Research and Education Clinical Center, Baltimore VA Medical Center, Baltimore, MD, USA

    Braxton D. Mitchell

  30. Division of Endocrinology, Diabetes, and Nutrition, Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA

    May E. Montasser

  31. Ministry of Health, Government of Samoa, Apia, Samoa

    Take Naseri

  32. Departments of Epidemiology, University of Washington, Seattle, WA, USA

    Bruce M. Psaty & Alexander P. Reiner

  33. Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA

    Bruce M. Psaty

  34. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Laura M. Raffield

  35. Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA

    Susan Redline

  36. Lutia I Puava Ae Mapu I Fagalele, Apia, Samoa

    Muagututi’a Sefuiva Reupena

  37. Department of Biostatistics, University of Washington, Seattle, WA, USA

    Kenneth M. Rice

  38. Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA

    Jennifer A. Smith & Wei Zhao

  39. Department of Medicine, Boston University School of Medicine, Boston, MA, USA

    Ramachandran S. Vasan

  40. Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA

    Cristen J. Willer

  41. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA

    Cristen J. Willer

  42. Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA

    Cristen J. Willer

  43. Division of Cardiology, Beth Israel Deaconess Medical Center, Boston, MA, USA

    James G. Wilson

  44. Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA

    Zilin Li

  45. Department of Statistics, Harvard University, Cambridge, MA, USA

    Xihong Lin

  46. New York Genome Center, New York, NY, USA

    Namiko Abe, Karen Bunting, Bo-Juen Chen, Soren Germer, Tanja Smith & Michael Zody

  47. University of Michigan, Ann Arbor, MI, USA

    Gonçalo Abecasis, Thomas Blackwell, Matthew Flickinger, Colin Gross, Sharon Kardia, Jonathon LeFaive, Jacob Pleiness, Daniel Taliun, Peter VandeHaar, Ketian Yu & Sebastian Zoellner

  48. Broad Institute, Cambridge, MA, USA

    Francois Aguet, Kristin Ardlie, Mark Chaffin, Seung Hoan Choi, Stacey Gabriel, Namrata Gupta, Carolina Roselli & Seyedeh Maryam Zekavat

  49. Cedars Sinai, Boston, MA, USA

    Christine Albert

  50. Children’s Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA

    Laura Almasy

  51. Emory University, Atlanta, GA, USA

    Alvaro Alonso, Rich Johnston, Lawrence S. Phillips & Zhaohui Qin

  52. University of Maryland, Baltimore, MD, USA

    Seth Ament, Amber Beitelshees, Christy Chang, Coleen Damcott, Scott Devine, Mao Fu, Da-Wei Gong, Yue Guan, Elliott Hong, Michael Kessler, Joshua Lewis, Patrick McArdle, Tim O’Connor, James Perry, Toni Pollin, Robert Reed, Kathleen Ryan, Amol Shetty, Elizabeth Streeten, Simeon Taylor & Huichun Xu

  53. University of Washington, Seattle, WA, USA

    Peter Anderson, Jai Broome, Colleen Davis, Leslie Emery, Chris Frazar, Stephanie M. Fullerton, Stephanie Gogarten, Deepti Jain, Craig Johnson, Alyna Khan, Cathy Laurie, Cecelia Laurie, David Levine, Josh Smith, Nona Sotoodehnia, Adrienne M. Stilp, Adam Szpiro, Timothy A. Thornton, David Tirschwell, Fei Fei Wang, Bruce Weir & Quenna Wong

  54. University of Mississippi Medical Center, Jackson, MI, USA

    Pramod Anugu, Lynette Ekunwe, Yan Gao, Hao Mei & Nancy Min

  55. National Institutes of Health, Bethesda, MD, USA

    Deborah Applebaum-Bowden

  56. Johns Hopkins University, Baltimore, MD, USA

    Dan Arking, Dimitrios Avramopoulos, Emily Barron-Casella, Terri Beaty, Lewis Becker, James Casella, Kimberly Jones, Barry Make, Rasika Mathias, Rakhi Naik, Ingo Ruczinski, Steven Salzberg, Margaret Taub & Dhananjay Vaidya

  57. Duke University, Durham, NC, USA

    Allison Ashley-Koch & Marilyn Telen

  58. University of Alabama, Birmingham, AL, USA

    Stella Aslibekyan, Bertha Hidalgo & Marguerite Ryan Irvin

  59. Stanford University, Stanford, CA, USA

    Tim Assimes, Chris Gignoux, Marco Perez & Michael Snyder

  60. Medical College of Wisconsin, Milwaukee, WI, USA

    Paul Auer

  61. Department of Medicine, Providence Health Care, Vancouver, CA, USA

    Najib Ayas

  62. Baylor College of Medicine Human Genome Sequencing Center, Houston, TX, USA

    Adithya Balasubramanian, Huyen Dinh, Shannon Dugan-Perez, Jesse Farek, Richard Gibbs, Yi Han, Jianhong Hu, Ziad Khan, Sandra Lee, Vipin Menon, Ginger Metcalf, Zeineen Momin, Donna Muzny, Caitlin Nessner, Osuji Nkechinyere, Geoffrey Okwuonu, Mahitha Rajendran, Sejal Salvi, Jireh Santibanez & Jennifer Watt

  63. Cleveland Clinic, Cleveland, OH, USA

    John Barnard & Serpil Erzurum

  64. Tempus, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

    Kathleen Barnes

  65. Columbia University, New York, NY, USA

    R. Graham Barr

  66. The Emmes Corporation, LTRC, Rockville, MD, USA

    Lucas Barwick

  67. Cleveland Clinic, Quantitative Health Sciences, Cleveland, OH, USA

    Gerald Beck

  68. Department of Medicine, Johns Hopkins University, Baltimore, MD, USA

    Diane Becker

  69. National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health, Bethesda, MD, USA

    Rebecca Beer, Weiniu Gan, Andrew Johnson, Dan Levy, James Luo, Julie Mikulla, George Papanicolaou & Pankaj Qasba

  70. Boston University, Massachusetts General Hospital, Boston University School of Medicine, Boston, MA, USA

    Emelia Benjamin

  71. University of Pittsburgh, Pittsburgh, PA, USA

    Takis Benos, Mark Gladwin, Ryan L. Minster & Frank Sciurba

  72. Fundação de Hematologia e Hemoterapia de PernambucoHemope, Recife, BR, USA

    Marcos Bezerra

  73. Department of Obstetrics and Gynecology, University of Utah, Salt Lake City, UT, USA

    Nathan Blue

  74. National Jewish Health, Denver, CO, USA

    Russell Bowler

  75. Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI, USA

    Ulrich Broeckel

  76. Department of Pediatrics, University of Texas Health at Houston, Houston, TX, USA

    Deborah Brown

  77. University of California, San Francisco, CA, USA

    Esteban Burchard & Ryan Hernandez

  78. Department of Biomedical Data Science, Stanford University, Stanford, CA, USA

    Carlos Bustamante

  79. Department of Biostatistics, University of Washington, Seattle, WA, USA

    Erin Buth, Matthew Conomos,  Ben Heavner, Susanne May, Caitlin McHugh, Sarah C. Nelson & Kayleen Williams

  80. University of Colorado at Denver, Denver, CO, USA

    Jonathan Cardwell, Sameer Chavan, Michelle Daya, Shanshan Gao, Daniel Grine, John Hokanson, Ethan Lange, Susan Mathai, Bonnie Neltner, Meher Preethi Boorgula, Pamela Russell, David Schwartz, Aniket Shetty, Tarik Walker, Avram Walts & Ivana Yang

  81. Brigham & Women’s Hospital, Boston, MA, USA

    Vincent Carey, Michael Cho, Dawn DeMeo, Auyon Ghosh, Brian Hobbs, Meryl LeBoff, Jiwon Lee, JoAnn Manson, Dandi Qiao, Edwin Silverman, Tamar Sofer, Jody Sylvia & Carla Wilson

  82. University of Montreal, Montreal, Quebec, Canada

    Julie Carrier

  83. Department of Medicine, University of Mississippi, Jackson, MI, USA

    April Carson

  84. Washington State University, Pullman, WA, USA

    Cara Carty

  85. University of California, Los Angeles, CA, USA

    Richard Casaburi, Carolyn Crandall & Karol Watson

  86. Brigham & Women’s Hospital, Boston, MA, USA

    Juan P. Casas Romero

  87. Department of Medicine, Brigham & Women’s Hospital, Boston, MA, USA

    Peter Castaldi & Matt Moll

  88. National Taiwan University, Taipei, Taiwan

    Yi-Cheng Chang

  89. Division of Preventive Medicine, Brigham & Women’s Hospital, Boston, MA, USA

    Daniel Chasman

  90. University of Virginia, Charlottesville, VA, USA

    Wei-Min Chen, Charles Farber, Josyf C. Mychaleckyj & Aakrosh Ratan

  91. Lundquist Institute, Torrance, CA, USA

    Yii-Der Ida Chen, Xiaohui Li & Henry Lin

  92. National Taiwan University, National Taiwan University Hospital, Taipei, Taiwan

    Lee-Ming Chuang

  93. Cleveland Clinic, Cleveland, OH, USA

    Mina Chung

  94. National Health Research Institute Taiwan, Miaoli County, Taiwan

    Ren-Hua Chung

  95. Broad Institute, Metabolomics Platform, Cambridge, MA, USA

    Clary Clish

  96. Department of Immunity and Immunology, Cleveland Clinic, Cleveland, OH, USA

    Suzy Comhair

  97. University of Vermont, Burlington, VT, USA

    Elaine Cornell & Jon Peter Durda

  98. National Jewish Health, Denver, CO, USA

    James Crapo, Elizabeth Regan & Snow Xueyan Zhao

  99. Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA

    Jeffrey Curtis

  100. Vitalant Research Institute, San Francisco, CA, USA

    Brian Custer

  101. University of Illinois at Chicago, Chicago, IL, USA

    Dawood Darbar

  102. University of Chicago, Chicago, IL, USA

    Sean David

  103. Mayo Clinic, Health Quantitative Sciences Research, Rochester, MI, USA

    Mariza de Andrade

  104. Cardiovascular Division, Department of Medicine, Washington University in St Louis, St. Louis, MO, USA

    Lisa de las Fuentes

  105. Vanderbilt University, Nashville, TN, USA

    Michael DeBaun

  106. University of Cincinnati, Cincinnati, OH, USA

    Ranjan Deka

  107. Baylor College of Medicine Human Genome Sequencing Center, Houston, TX, USA

    Harsha Doddapaneni

  108. University of North Carolina, Chapel Hill, NC, USA

    Qing Duan, Yun Li & Kari North

  109. Department of Genetics, Washington University in St Louis, St Louis, MO, USA

    Susan K. Dutcher

  110. Brown University, Providence, RI, USA

    Charles Eaton

  111. Channing Division of Network Medicine, Harvard University, Cambridge, MA, USA

    Adel El Boueiz

  112. Massachusetts General Hospital, Boston, MA, USA

    Patrick Ellinor, Steven Lubitz & Lu-Chen Weng

  113. Center for Genes, Environment and Health, National Jewish Health, Denver, CO, USA

    Tasha Fingerlin

  114. University of Texas Health at Houston, Houston, TX, USA

    Myriam Fornage & James Hixson

  115. Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA

    Nora Franceschini

  116. Washington University in St Louis, St Louis, MI, USA

    Lucinda Fulton, C. Charles Gu, D. C. Rao, Karen Schwander & Yun Ju Sung

  117. Fred Hutchinson Cancer Center, Seattle, WA, USA

    Margery Gass

  118. New York Genome Center, New York City, NY, USA

    Heather Geiger, Melissa Marton, Nicolas Robine, Alexi Runnels & Lara Winterkorn

  119. Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Bruce Gelb, Eimear Kenny, Girish Nadkarni & Michael Preuss

  120. University of Pittsburgh, Pittsburgh, PA, USA

    Mark Geraci

  121. Beth Israel Deaconess Medical Center, Boston, MA, USA

    Robert Gerszten

  122. Department of Psychiatry, Harvard Medical School, Boston Children’s Hospital, Boston, MA, USA

    David Glahn

  123. University of Colorado Anschutz Medical Campus, Aurora, CO, USA

    Sharon Graw, Luisa Mestroni & Matthew Taylor

  124. Department of Obstetrics and Gynecology, Mass General Brigham, Boston, MA, USA

    Kathryn J. Gray

  125. Department of Cardiology, University of Mississippi, Jackson, MI, USA

    Michael Hall

  126. Department of Medicine, University of Calgary, Calgary, Alberta, Canada

    Patrick Hanly

  127. Department of Genetics, University of Maryland, Philadelphia, PA, USA

    Daniel Harris

  128. Department of Chronic Disease Epidemiology, Yale University, New Haven, CT, USA

    Nicola L. Hawley

  129. Tulane University, New Orleans, LA, USA

    Jiang He

  130. Department of Epidemiology, University of Washington, Seattle, WA, USA

    Susan Heckbert & Nicholas Smith

  131. Wake Forest Baptist Health, Winston-Salem, NC, USA

    David Herrington

  132. Channing Division of Network Medicine, Brigham & Women’s Hospital, Boston, MA, USA

    Craig Hersh

  133. University of Iowa, Iowa City, IA, USA

    Karin Hoth & Robert Wallace

  134. National Health Research Institute Taiwan (NHRI), Institute of Population Health Sciences, Miaoli, Taiwan

    Chao (Agnes) Hsiung

  135. Tri-Service General Hospital National Defense Medical Center, Taipei, Taiwan

    Yi-Jen Hung

  136. Blood Works Northwest, Seattle, WA, USA

    Haley Huston & Sarah Ruuska

  137. Taichung Veterans General Hospital Taiwan, Taichung City, Taiwan

    Chii Min Hwu, Wen-Jane Lee & Wayne Hui-Heng Sheu

  138. Division of Endocrinology, Diabetes and Metabolism, Department of Internal Medicine, Oklahoma State University Medical Center, Columbus, OH, USA

    Rebecca Jackson

  139. National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health, Bethesda, MD, USA

    Cashell Jaquish

  140. Bloodworks Northwest Research Institute, Seattle, WA, USA

    Jill Johnsen

  141. Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA

    Hyun Min Kang & Joshua Weinstock

  142. Albert Einstein College of Medicine, New York, NY, USA

    Robert Kaplan & Sylvia Smoller

  143. University of California, San Francisco, CA, USA

    Shannon Kelly

  144. Harvard University, Cambridge, MA, USA

    Wonji Kim & Sean McFarland

  145. McGill University, Montréal, Quebec, Canada

    John Kimoff

  146. Department of Epidemiology, University of Colorado at Denver, Aurora, CO, USA

    Greg Kinney

  147. Department of Medicine, Blood Works Northwest, Seattle, WA, USA

    Barbara Konkle

  148. Loyola University Parkinson School of Health Sciences and Public Health, Loyola University, Maywood, IL, USA

    Holly Kramer

  149. Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA

    Christoph Lange

  150. Boston University, University of Massachusetts Chan Medical School, Worcester, MA, USA

    Honghuang Lin

  151. Department of Epidemiology and Medicine, Brown University, Providence, RI, USA

    Simin Liu

  152. Department of Cardiology, Duke University, Durham, NC, USA

    Yongmei Liu

  153. Stanford University, Cardiovascular Institute, Stanford, CA, USA

    Yu Liu

  154. Icahn School of Medicine at Mount Sinai, The Charles Bronfman Institute for Personalized Medicine, New York, NY, USA

    Ruth J. F. Loos

  155. Boston University, Boston, MA, USA

    Kathryn Lunetta

  156. Division of Pulmonary, Critical Care and Sleep Medicine, Ohio State University, Columbus, OH, USA

    Ulysses Magalang

  157. University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA

    Michael Mahaney

  158. Broad Institute, Harvard University, Massachusetts General Hospital, Cambridge, MA, USA

    Alisa Manning

  159. University of Alabama at Birmingham, Birmingham, AL, USA

    Merry-Lynn McDonald

  160. Department of Genome Sciences, University of Washington, Seattle, WA, USA

    Daniel McGoldrick

  161. RTI International, Research Triangle Park, USA

    Becky McNeil

  162. Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

    James Meigs

  163. University of Arizona, Tucson, AZ, USA

    Deborah A. Meyers

  164. Stanford University, Center For Sleep Sciences and Medicine, Palo Alto, CA, USA

    Emmanuel Mignot

  165. National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA

    Mollie Minear

  166. Oklahoma Medical Research Foundation, Genes and Human Disease Research Program, Oklahoma City, OK, USA

    Courtney Montgomery

  167. Howard University, Washington, DC, USA

    Sergei Nekhai

  168. Department of Genome Sciences, University of Washington, Seattle, WA, USA

    Deborah Nickerson

  169. University at Buffalo, Buffalo, NY, USA

    Heather Ochs-Balcom

  170. Division of Sleep Medicine, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Allan Pack

  171. Stanford University, Stanford Cardiovascular Institute, Stanford, CA, USA

    David T. Paik & Joseph Wu

  172. University of Minnesota, Minneapolis, MN, USA

    James Pankow, Michael Tsai & Scott Vrieze

  173. Division of Biostatistics and Epidemiology, RTI International, Research Triangle Park, NC, USA

    Cora Parker

  174. University of Texas Rio Grande Valley School of Medicine, Edinburg, TX, USA

    Juan Manuel Peralta

  175. Department of Epidemiology, University of Washington School of Medicine, Seattle, WA, USA

    Ulrike Peters

  176. Department of Cardiology/Medicine, Johns Hopkins University, Baltimore, MD, USA

    Wendy Post

  177. Department of Medicine, University of Colorado at Denver, Denver, CO, USA

    Julia Powers Becker

  178. University of Colorado at Denver, CCPM, Denver, CO, USA

    Nicholas Rafaels

  179. Northwestern University, Chicago, IL, USA

    Laura Rasmussen-Torvik

  180. New York Genome Center, New York City, NY, USA

    Catherine Reeves

  181. University of Ottawa, Sleep Research Unit, University of Ottawa Institute for Mental Health Research, Ottawa, Ontario, Canada

    Rebecca Robillard

  182. Department of Medicine, Pharmacology, Biomedical Informatics, Vanderbilt University, Nashville, TN, USA

    Dan Roden

  183. Universidade de Sao Paulo, Faculdade de Medicina, Sao Paulo, BR, Brazil

    Ester Cerdeira Sabino

  184. Columbia University, New York, NY, USA

    Danish Saleheen

  185. Department of Pathology, University of Maryland, Seattle, WA, USA

    Shabnam Salimi

  186. Lundquist Institute, TGPS, Torrance, CA, USA

    Kevin Sandow

  187. Division of Hematology/Oncology, Harvard University, Boston, MA, USA

    Vijay G. Sankaran

  188. Department of Genetics, Harvard Medical School, Boston, MA, USA

    Christine Seidman

  189. Harvard Medical School, Boston, MA, USA

    Jonathan Seidman

  190. Université Laval, Quebec City, Quebec, Canada

    Frédéric Sériès

  191. Department of Pediatrics, Emory University, Atlanta, GA, USA

    Vivien Sheehan

  192. Department of Human Genetics, Emory University, Atlanta, GA, USA

    Stephanie L. Sherman

  193. Department of Medicine/Cardiology, Vanderbilt University, Nashville, TN, USA

    M. Benjamin Shoemaker

  194. UMass Memorial Medical Center, Worcester, MA, USA

    Brian Silver

  195. University of Saskatchewan, Saskatoon, Saskatchewan, Canada

    Robert Skomro

  196. University of Michigan, Ann Arbor, MI, USA

    Albert Vernon Smith

  197. Department of Biostatistical Sciences, Wake Forest Baptist Health, Winston-Salem, NC, USA

    Beverly Snively

  198. Department of Genomic Cardiology, University of Colorado at Denver, Aurora, CO, USA

    Garrett Storm

  199. Channing Division of Network Medicine, Department of Medicine, Brigham & Women’s Hospital, Boston, MA, USA

    Jessica Lasky Su

  200. Department of Genetics, Stanford University, Stanford, CA, USA

    Hua Tang

  201. Department of Genome Sciences, University of Washington, Seattle, WA, USA

    Machiko Threlkeld

  202. Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Lesley Tinker

  203. Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA

    Sarah Tishkoff

  204. Department of Biostatistics, University of Alabama, Birmingham, AL, USA

    Hemant Tiwari

  205. Department of Biostatistics, University of Washington, Seattle, WA, USA

    Catherine Tong

  206. Department of Pathology and Laboratory Medicine, University of Vermont, Burlington, VT, USA

    Russell Tracy

  207. USC Methylation Characterization Center, University of Southern California, Los Angeles, CA, USA

    David Van Den Berg

  208. Brigham & Women’s Hospital, Mass General Brigham, Boston, MA, USA

    Heming Wang

  209. University of Michigan, Ann Arbor, MI, USA

    Jiongming Wang

  210. Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA

    Daniel E. Weeks

  211. Channing Division of Network Medicine, Department of Medicine, Brigham & Women’s Hospital, Boston, MA, USA

    Scott T. Weiss

  212. Department of Epidemiology, Indiana University, Indianapolis, IN, USA

    Jennifer Wessel

  213. Henry Ford Health System, Detroit, MI, USA

    L. Keoki Williams

  214. Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA

    Yingze Zhang

  215. Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA

    Xiaofeng Zhu

  216. University of California, San Francisco, CA, USA

    Elad Ziv

Authors
  1. Xihao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Corbin Quick
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hufeng Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sheila M. Gaynor
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yaowu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Han Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Margaret Sunitha Selvaraj
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ryan Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Rounak Dey
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Donna K. Arnett
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Lawrence F. Bielak
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Joshua C. Bis
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. John Blangero
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Eric Boerwinkle
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Donald W. Bowden
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Jennifer A. Brody
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Brian E. Cade
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Adolfo Correa
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. L. Adrienne Cupples
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Joanne E. Curran
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Paul S. de Vries
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Ravindranath Duggirala
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Barry I. Freedman
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Harald H. H. Göring
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Xiuqing Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Jeffrey Haessler
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Rita R. Kalyani
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Charles Kooperberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Brian G. Kral
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Leslie A. Lange
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Ani Manichaikul
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. Lisa W. Martin
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. Stephen T. McGarvey
    View author publications

    You can also search for this author in PubMed Google Scholar

  34. Braxton D. Mitchell
    View author publications

    You can also search for this author in PubMed Google Scholar

  35. May E. Montasser
    View author publications

    You can also search for this author in PubMed Google Scholar

  36. Alanna C. Morrison
    View author publications

    You can also search for this author in PubMed Google Scholar

  37. Take Naseri
    View author publications

    You can also search for this author in PubMed Google Scholar

  38. Jeffrey R. O’Connell
    View author publications

    You can also search for this author in PubMed Google Scholar

  39. Nicholette D. Palmer
    View author publications

    You can also search for this author in PubMed Google Scholar

  40. Patricia A. Peyser
    View author publications

    You can also search for this author in PubMed Google Scholar

  41. Bruce M. Psaty
    View author publications

    You can also search for this author in PubMed Google Scholar

  42. Laura M. Raffield
    View author publications

    You can also search for this author in PubMed Google Scholar

  43. Susan Redline
    View author publications

    You can also search for this author in PubMed Google Scholar

  44. Alexander P. Reiner
    View author publications

    You can also search for this author in PubMed Google Scholar

  45. Muagututi’a Sefuiva Reupena
    View author publications

    You can also search for this author in PubMed Google Scholar

  46. Kenneth M. Rice
    View author publications

    You can also search for this author in PubMed Google Scholar

  47. Stephen S. Rich
    View author publications

    You can also search for this author in PubMed Google Scholar

  48. Colleen M. Sitlani
    View author publications

    You can also search for this author in PubMed Google Scholar

  49. Jennifer A. Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  50. Kent D. Taylor
    View author publications

    You can also search for this author in PubMed Google Scholar

  51. Ramachandran S. Vasan
    View author publications

    You can also search for this author in PubMed Google Scholar

  52. Cristen J. Willer
    View author publications

    You can also search for this author in PubMed Google Scholar

  53. James G. Wilson
    View author publications

    You can also search for this author in PubMed Google Scholar

  54. Lisa R. Yanek
    View author publications

    You can also search for this author in PubMed Google Scholar

  55. Wei Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  56. Jerome I. Rotter
    View author publications

    You can also search for this author in PubMed Google Scholar

  57. Pradeep Natarajan
    View author publications

    You can also search for this author in PubMed Google Scholar

  58. Gina M. Peloso
    View author publications

    You can also search for this author in PubMed Google Scholar

  59. Zilin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  60. Xihong Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working Group

  • Namiko Abe
  • , Gonçalo Abecasis
  • , Francois Aguet
  • , Christine Albert
  • , Laura Almasy
  • , Alvaro Alonso
  • , Seth Ament
  • , Peter Anderson
  • , Pramod Anugu
  • , Deborah Applebaum-Bowden
  • , Kristin Ardlie
  • ,  Dan Arking
  • , Allison Ashley-Koch
  • , Stella Aslibekyan
  • , Tim Assimes
  • , Paul Auer
  • , Dimitrios Avramopoulos
  • , Najib Ayas
  • , Adithya Balasubramanian
  • , John Barnard
  • , Kathleen Barnes
  • , R. Graham Barr
  • , Emily Barron-Casella
  • , Lucas Barwick
  • , Terri Beaty
  • , Gerald Beck
  • , Diane Becker
  • , Lewis Becker
  • , Rebecca Beer
  • , Amber Beitelshees
  • , Emelia Benjamin
  • , Takis Benos
  • , Marcos Bezerra
  • , Thomas Blackwell
  • , Nathan Blue
  • , Russell Bowler
  • , Ulrich Broeckel
  • , Jai Broome
  • , Deborah Brown
  • , Karen Bunting
  • , Esteban Burchard
  • , Carlos Bustamante
  • , Erin Buth
  • , Jonathan Cardwell
  • , Vincent Carey
  • , Julie Carrier
  • , April Carson
  • , Cara Carty
  • , Richard Casaburi
  • , Juan P. Casas Romero
  • , James Casella
  • , Peter Castaldi
  • , Mark Chaffin
  • , Christy Chang
  • , Yi-Cheng Chang
  • , Daniel Chasman
  • , Sameer Chavan
  • , Bo-Juen Chen
  • , Wei-Min Chen
  • , Yii-Der Ida Chen
  • , Michael Cho
  • , Seung Hoan Choi
  • , Lee-Ming Chuang
  • , Mina Chung
  • , Ren-Hua Chung
  • , Clary Clish
  • , Suzy Comhair
  • , Matthew Conomos
  • , Elaine Cornell
  • , Carolyn Crandall
  • , James Crapo
  • , Jeffrey Curtis
  • , Brian Custer
  • , Coleen Damcott
  • , Dawood Darbar
  • , Sean David
  • , Colleen Davis
  • , Michelle Daya
  • , Mariza de Andrade
  • , Lisa de las Fuentes
  • , Michael DeBaun
  • , Ranjan Deka
  • , Dawn DeMeo
  • , Scott Devine
  • , Huyen Dinh
  • , Harsha Doddapaneni
  • , Qing Duan
  • , Shannon Dugan-Perez
  • , Jon Peter Durda
  • , Susan K. Dutcher
  • , Charles Eaton
  • , Lynette Ekunwe
  • , Adel El Boueiz
  • , Patrick Ellinor
  • , Leslie Emery
  • , Serpil Erzurum
  • , Charles Farber
  • , Jesse Farek
  • , Tasha Fingerlin
  • , Matthew Flickinger
  • , Myriam Fornage
  • , Nora Franceschini
  • , Chris Frazar
  • , Mao Fu
  • , Stephanie M. Fullerton
  • , Lucinda Fulton
  • , Stacey Gabriel
  • , Weiniu Gan
  • , Shanshan Gao
  • , Yan Gao
  • , Margery Gass
  • , Heather Geiger
  • , Bruce Gelb
  • , Mark Geraci
  • , Soren Germer
  • , Robert Gerszten
  • , Auyon Ghosh
  • , Richard Gibbs
  • , Chris Gignoux
  • , Mark Gladwin
  • , David Glahn
  • , Stephanie Gogarten
  • , Da-Wei Gong
  • , Sharon Graw
  • , Kathryn J. Gray
  • , Daniel Grine
  • , Colin Gross
  • , C. Charles Gu
  • , Yue Guan
  • , Namrata Gupta
  • , Michael Hall
  • , Yi Han
  • , Patrick Hanly
  • , Daniel Harris
  • , Nicola L. Hawley
  • , Jiang He
  • ,  Ben Heavner
  • , Susan Heckbert
  • , Ryan Hernandez
  • , David Herrington
  • , Craig Hersh
  • , Bertha Hidalgo
  • , James Hixson
  • , Brian Hobbs
  • , John Hokanson
  • , Elliott Hong
  • , Karin Hoth
  • , Chao (Agnes) Hsiung
  • , Jianhong Hu
  • , Yi-Jen Hung
  • , Haley Huston
  • , Chii Min Hwu
  • , Marguerite Ryan Irvin
  • , Rebecca Jackson
  • , Deepti Jain
  • , Cashell Jaquish
  • , Jill Johnsen
  • , Andrew Johnson
  • , Craig Johnson
  • , Rich Johnston
  • , Kimberly Jones
  • , Hyun Min Kang
  • , Robert Kaplan
  • , Sharon Kardia
  • , Shannon Kelly
  • , Eimear Kenny
  • , Michael Kessler
  • , Alyna Khan
  • , Ziad Khan
  • , Wonji Kim
  • , John Kimoff
  • , Greg Kinney
  • , Barbara Konkle
  • , Holly Kramer
  • , Christoph Lange
  • , Ethan Lange
  • , Cathy Laurie
  • , Cecelia Laurie
  • , Meryl LeBoff
  • , Jiwon Lee
  • , Sandra Lee
  • , Wen-Jane Lee
  • , Jonathon LeFaive
  • , David Levine
  • , Dan Levy
  • , Joshua Lewis
  • , Xiaohui Li
  • , Yun Li
  • , Henry Lin
  • , Honghuang Lin
  • , Simin Liu
  • , Yongmei Liu
  • , Yu Liu
  • , Ruth J. F. Loos
  • , Steven Lubitz
  • , Kathryn Lunetta
  • , James Luo
  • , Ulysses Magalang
  • , Michael Mahaney
  • , Barry Make
  • , Alisa Manning
  • , JoAnn Manson
  • , Melissa Marton
  • , Susan Mathai
  • , Rasika Mathias
  • , Susanne May
  • , Patrick McArdle
  • , Merry-Lynn McDonald
  • , Sean McFarland
  • , Daniel McGoldrick
  • , Caitlin McHugh
  • , Becky McNeil
  • , Hao Mei
  • , James Meigs
  • , Vipin Menon
  • , Luisa Mestroni
  • , Ginger Metcalf
  • , Deborah A. Meyers
  • , Emmanuel Mignot
  • , Julie Mikulla
  • , Nancy Min
  • , Mollie Minear
  • , Ryan L. Minster
  • , Matt Moll
  • , Zeineen Momin
  • , Courtney Montgomery
  • , Donna Muzny
  • , Josyf C. Mychaleckyj
  • , Girish Nadkarni
  • , Rakhi Naik
  • , Sergei Nekhai
  • , Sarah C. Nelson
  • , Bonnie Neltner
  • , Caitlin Nessner
  • , Deborah Nickerson
  • , Osuji Nkechinyere
  • , Kari North
  • , Tim O’Connor
  • , Heather Ochs-Balcom
  • , Geoffrey Okwuonu
  • , Allan Pack
  • , David T. Paik
  • , James Pankow
  • , George Papanicolaou
  • , Cora Parker
  • , Juan Manuel Peralta
  • , Marco Perez
  • , James Perry
  • , Ulrike Peters
  • , Lawrence S. Phillips
  • , Jacob Pleiness
  • , Toni Pollin
  • , Wendy Post
  • , Julia Powers Becker
  • , Meher Preethi Boorgula
  • , Michael Preuss
  • , Pankaj Qasba
  • , Dandi Qiao
  • , Zhaohui Qin
  • , Nicholas Rafaels
  • , Mahitha Rajendran
  • , D. C. Rao
  • , Laura Rasmussen-Torvik
  • , Aakrosh Ratan
  • , Robert Reed
  • , Catherine Reeves
  • , Elizabeth Regan
  • , Rebecca Robillard
  • , Nicolas Robine
  • ,  Dan Roden
  • , Carolina Roselli
  • , Ingo Ruczinski
  • , Alexi Runnels
  • , Pamela Russell
  • , Sarah Ruuska
  • , Kathleen Ryan
  • , Ester Cerdeira Sabino
  • , Danish Saleheen
  • , Shabnam Salimi
  • , Sejal Salvi
  • , Steven Salzberg
  • , Kevin Sandow
  • , Vijay G. Sankaran
  • , Jireh Santibanez
  • , Karen Schwander
  • , David Schwartz
  • , Frank Sciurba
  • , Christine Seidman
  • , Jonathan Seidman
  • , Frédéric Sériès
  • , Vivien Sheehan
  • , Stephanie L. Sherman
  • , Amol Shetty
  • , Aniket Shetty
  • , Wayne Hui-Heng Sheu
  • , M. Benjamin Shoemaker
  • , Brian Silver
  • , Edwin Silverman
  • , Robert Skomro
  • , Albert Vernon Smith
  • , Josh Smith
  • , Nicholas Smith
  • , Tanja Smith
  • , Sylvia Smoller
  • , Beverly Snively
  • , Michael Snyder
  • , Tamar Sofer
  • , Nona Sotoodehnia
  • , Adrienne M. Stilp
  • , Garrett Storm
  • , Elizabeth Streeten
  • , Jessica Lasky Su
  • , Yun Ju Sung
  • , Jody Sylvia
  • , Adam Szpiro
  • , Daniel Taliun
  • , Hua Tang
  • , Margaret Taub
  • , Matthew Taylor
  • , Simeon Taylor
  • , Marilyn Telen
  • , Timothy A. Thornton
  • , Machiko Threlkeld
  • , Lesley Tinker
  • , David Tirschwell
  • , Sarah Tishkoff
  • , Hemant Tiwari
  • , Catherine Tong
  • , Russell Tracy
  • , Michael Tsai
  • , Dhananjay Vaidya
  • , David Van Den Berg
  • , Peter VandeHaar
  • , Scott Vrieze
  • , Tarik Walker
  • , Robert Wallace
  • , Avram Walts
  • , Fei Fei Wang
  • , Heming Wang
  • , Jiongming Wang
  • , Karol Watson
  • , Jennifer Watt
  • , Daniel E. Weeks
  • , Joshua Weinstock
  • , Bruce Weir
  • , Scott T. Weiss
  • , Lu-Chen Weng
  • , Jennifer Wessel
  • , Kayleen Williams
  • , L. Keoki Williams
  • , Carla Wilson
  • , Lara Winterkorn
  • , Quenna Wong
  • , Joseph Wu
  • , Huichun Xu
  • , Ivana Yang
  • , Ketian Yu
  • , Seyedeh Maryam Zekavat
  • , Yingze Zhang
  • , Snow Xueyan Zhao
  • , Xiaofeng Zhu
  • , Elad Ziv
  • , Michael Zody
  •  & Sebastian Zoellner

Contributions

X. Li, C.Q., G.M.P., Z.L. and X.L. designed the experiments. X.Li, C.Q., Z.L. and X. Lin performed the experiments. X. Li, C.Q., H.Z., S.M.G., Y.L., H.C., M.S.S., R.S., R.D., D.K.A., L.F.B., J.C.B., J.B., E.B., D.W.B., J.A.B., B.E.C., A.C., L.A.C., J.E.C., P.S.d.V., R.D., B.I.F., H.H.H.G., X.G., J.H., R.R.K., C.K., B.G.K., L.A.L., A.M., L.W.M., S.T.M., B.D.M., M.E.M., A.C.M., T.N., J.R.O., N.D.P., P.A.P., B.M.P., L.M.R., S.R., A.P.R., M.S.R., K.M.R., S.S.R., C.M.S, J.A.S., K.D.T., R.S.V., C.J.W., J.G.W., L.R.Y., W.Z., J.I.R., P.N., G.M.P., Z.L. and X. Lin acquired, analyzed or interpreted data. G.M.P., P.N. and the NHLBI TOPMed Lipids Working Group provided administrative, technical or material support. X. Li, Z.L. and X. Lin drafted the manuscript and revised it according to suggestions by the co-authors. All authors critically reviewed the manuscript, suggested revisions as needed and approved the final version.

Corresponding authors

Correspondence to Zilin Li or Xihong Lin.

Ethics declarations

Competing interests

S.M.G. is now an employee of Regeneron Genetics Center. For B.D.M., The Amish Research Program receives partial support from Regeneron Pharmaceuticals. M.E.M. reports grant from Regeneron Pharmaceutical unrelated to the present work. B.M.P. serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. L.M.R. is a consultant for the TOPMed Administrative Coordinating Center (through Westat). For S.R., Jazz Pharma, Eli Lilly, Apnimed, unrelated to the present work. The spouse of C.J.W. works at Regeneron Pharmaceuticals. P.N. reports investigator-initiated grants from Amgen, Apple, AstraZeneca, Boston Scientific and Novartis, personal fees from Apple, AstraZeneca, Blackstone Life Sciences, Foresite Labs, Novartis, Roche/Genentech, is a cofounder of TenSixteen Bio, is a shareholder of geneXwell and TenSixteen Bio, and spousal employment at Vertex, all unrelated to the present work. X. Lin is a consultant of AbbVie Pharmaceuticals and Verily Life Sciences. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks Christoph Lippert and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Quantile-quantile plots for gene-centric unconditional meta-analysis of lipid traits LDL-C, HDL-C, TG and TC using TOPMed WGS data (n = 30,138).

MetaSTAAR-O is a two-sided test. Different symbols represent the MetaSTAAR-O P values of different functional categories of individual genes (putative loss-of-function, missense, synonymous, promoter and enhancer). The promoter and enhancer of a gene are the promoter and the GeneHancer region that overlap with CAGE sites for a given gene, respectively (Methods). Four lipid traits were analyzed using MetaSTAAR-O: LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglycerides; and TC, total cholesterol.

Extended Data Fig. 2 Manhattan plots for gene-centric unconditional meta-analysis of lipid traits LDL-C, HDL-C, TG and TC using TOPMed WGS data (n = 30,138).

The horizontal line indicates the genome-wide MetaSTAAR-O P value threshold of 5.00 × 10−7. The significant threshold is defined by multiple comparisons using the Bonferroni correction (0.05/(20,000×5) = 5.00 × 10−7). MetaSTAAR-O is a two-sided test. Different symbols represent the MetaSTAAR-O P values of different functional categories of individual genes (putative loss-of-function, missense, synonymous, promoter and enhancer). The promoter and enhancer of a gene are the promoter and the GeneHancer region that overlap with CAGE sites for a given gene, respectively (Methods). Four lipid traits were analyzed using MetaSTAAR-O: LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglycerides; TC, total cholesterol.

Extended Data Fig. 3 Scatterplots comparing gene-centric unconditional meta-analysis P values from MetaSTAAR-O with STAAR-O from the joint analysis of pooled individual-level data (STAAR-O-Pooled) of lipid traits LDL-C, HDL-C, TG and TC using TOPMed WGS data (n = 30,138).

Each dot represents a functional category of a gene with x-axis label being the −log10(P) of STAAR-O-Pooled and y-axis label being the −log10(P) of MetaSTAAR-O (n = 30,138). The horizontal and vertical lines indicate the genome-wide P value threshold of 5.00 × 10−7. The significant threshold is defined by multiple comparisons using the Bonferroni correction (0.05/(20,000×5) = 5.00 × 10−7). Both MetaSTAAR and STAAR are two-sided tests. LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglycerides; TC, total cholesterol.

Extended Data Fig. 4 Scatterplot of P values comparing MetaSTAAR-O to Burden-MS, SKAT-MS and ACAT-V-MS (MS is short for MetaSTAAR) for quantitative and dichotomous traits when 15% of rare variants are causal variants.

In each simulation replicate, a 2-kb region was randomly selected as the signal region. Within each signal region, variants were randomly generated to be causal based on a multiple logistic model and on average there were 15% causal variants in the signal region. The effect sizes of causal variants were βj = c0|log10MAFj|. For quantitative traits, c0 = 0.07; for dichotomous traits, c0 = 0.11. All causal variants had positive effect sizes. Power was estimated as the proportion of the P values less than α = 10−7 based on 104 replicates. Burden-MS, SKAT-MS, ACAT-V-MS and MetaSTAAR-O are two-sided tests. Five studies were included in meta-analysis, each with a sample size of 10,000.

Supplementary information

Supplementary information

Supplementary Figs. 1–4 and Supplementary note

Reporting Summary

Peer Review File

Supplementary Table 1

Supplementary Tables 1–9.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, X., Quick, C., Zhou, H. et al. Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies. Nat Genet 55, 154–164 (2023). https://doi.org/10.1038/s41588-022-01225-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-022-01225-6

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing